In this work, we use Learning from Demonstration framework to train an agent to learn the task of driving using high-dimensional observations in form of images. Learning from Demonstration along with deep function approximators have been used to tackle a lot of problems in robotics like indoor mobile robot navigation [1]}, quad-rotor control in forest trials [2]}, robot-arm manipulation [3]}, [4]}, [5]} among others. The closest to our work are the works of [6]} who show autonomous lane following using a single trained network, [7]} who demonstrate autonomous driving in CARLA using an additional conditional input from a high-level planner, [8]} who compare various contemporary networks for autonomous driving tasks and [9]} who demonstrate multi-task and multi-modal behavior for autonomous driving.
